Blend modes for mineralogy images

ABSTRACT

Optimized blending mode for mineralogy images. A luminosity value is determined for a pixel in a base layer or top layer mineralogy image. An image weighting value is determined from the luminosity value and an optional mixing parameter. A multiply value is determined by multiplying the base and top layer pixel values. An overlay value is determined from twice the multiply value if the value of one of the base layer or top layer pixel values is over a threshold, otherwise it is determined by inverting twice the product of the inverted top layer pixel value with the inverted base layer pixel value. A blended image pixel value is determined by adding the multiply value weighted with the image weighting value and the overlay value weighted with the inverted image weighting value.

This application is a Continuation of U.S. patent application Ser. No.14/204,485, filed Mar. 11, 2014, which is hereby incorporated byreference.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the generation and display ofmineralogy images, and in particular to a method for automaticallyblending mineralogy images of a sample, including textural, elementaland spatial mineral identification images.

BACKGROUND OF THE INVENTION

To properly understand and interpret the mineralogy of a sample,knowledge of both the textural information of the sample and the spatialdistribution of minerals in the sample is required.

The textural information of a geological sample refers to the size,shape and arrangement of grains or crystals within the sample, thehomogeneity of the sample, its degree of isotropy, and the distributionof elements within the minerals The textural information can bemeasured, for example, by scanning the sample with a high energy beam ofcharged particles, and measuring at each scanned position the intensityof back-scattered electrons (“BSE's”) that are received at an electrondetector. The measured BSE intensities are indicators of the averageatomic number of elements in the sample as a function of scan position.A plot of the intensity versus scan position can be presented as a greyscale image, where each pixel in the image is a shade of grey thatcorresponds to the measured BSE intensity at the scanned position. Theimage so obtained will be darkest where the measured BSE intensity isleast, and lightest where the measured BSE intensity is greatest. Ofcourse, this color scheme can be inverted so that the image will belightest where the measured BSE intensity is least, and darkest wherethe measured BSE intensity is greatest.

In addition to BSE intensity, other forms of textural information caninclude representation of the rate of x-ray production at each pixel.This is measured by scanning the sample with a high energy beam ofcharged particles, and measuring the intensity of x-rays produced by thesample and received by an x-ray detector. The x-rays are produced whenthe charged particles dislodge electrons in the sample, and electronsfrom higher energy shells fall into the energy shells vacated by thedislodged electrons. The rate at which the x-rays are produced isroughly proportional to the volume and atomic number of the material inthe sample. A plot of the x-ray generation rate versus scan position canbe presented as a grey scale image, where each pixel in the image is ashade of grey corresponding to the measured rate of x-ray production ateach scan position. The image so obtained will be darkest where themeasured x-ray production rate is lowest, and brightest where themeasured x-ray production rate is highest.

The spatial distribution of minerals in the sample is a plot of theminerals identified in the sample as a function of sample position. Itcan be generated by scanning the sample with a high energy beam, andmeasuring the energy distribution of x-rays emitted from the sample as afunction of scan position. On a per pixel basis, these energydistributions can be fitted and/or compared to a catalog of energydistributions obtained from pure elements or pure minerals in order toidentify the elements and/or minerals in the sample at each scannedposition. Techniques for identifying minerals based on a catalog ofelemental x-ray spectra are disclosed, for example, in U.S. patentapplication Ser. No. 12/866,697, filed on Feb. 6, 2009, which is hereinincorporated by reference in its entirety. Techniques for identifyingminerals based on a catalog of mineral x-ray spectra are disclosed, forexample, in U.S. patent application Ser. No. 14/073,523, filed on Nov.6, 2013, which is herein incorporated by reference in its entirety.Different colors can be assigned to different minerals in the catalog,and an image of the spatial mineral distribution in the sample can begenerated by plotting the colors of identified minerals as a function ofscanned position.

Since proper understanding of the mineralogy of a sample requiresknowledge of both the textural information of the sample and the spatialdistribution of minerals in the sample, methods are needed to combinethe textural and spatial mineral distribution images into a singlecomposite image that reveals both the spatial mineral distribution andtextural information about the sample.

SUMMARY OF THE INVENTION

An object of the invention is to provide an automated mechanism to allowdata from multiple detectors to be simultaneously visualized withoutlosing information from either data source.

In one aspect, the invention features a computer implemented method forblending mineralogy images, including a base layer mineralogy image anda top layer mineralogy image. For each pixel in the base or top layermineralogy images a base layer value is determined from the pixel in thebase layer mineralogy image, and a top layer value is determined fromthe pixel in the top layer mineralogy image. A luminosity value isdetermined from one of the base layer or top layer values. A multiplyvalue is determined by multiplying the base and top layer values. Anoverlay value is determined by doubling the multiply value if one of thebase layer or top layer values is less than a threshold, and byotherwise inverting twice the product of the inverted top layer valuewith the inverted bottom layer value. An image blending weight isdetermined from the luminosity value. A blended image value isdetermined as a weighted average of the multiply value and the overlayvalue, wherein the multiply value is weighted by the image blendingweight and the overlay value is weighted by the inverted image blendingweight.

Implementations of the invention may include one or more of thefollowing features. The base layer mineralogy image can be a texturalmineralogy image and the top layer mineralogy image can be a spatialmineral distribution image. The textural mineralogy image can be animage of the intensity of back-scattered electrons or an image of theintensity of generated x-rays. The image blending weight can bedetermined by multiplying the luminosity value by a mixing parameter. Ablended image, where each pixel of the blended image has a blended imagevalue determined from the base layer value and top layer value, can bedisplayed on a display device.

In another aspect, the invention features a computer implemented methodfor blending mineralogy images of a sample, including an elementaldistribution image of the sample, and a mineral distribution image ofthe sample. A blended image of the sample can be generated by blendingthe elemental and mineral distribution images of the sample.

Implementations of the invention may include one or more of thefollowing features. Each pixel in the blended image of the sample can begenerated from each pixel in the elemental and mineral distributionimages. A base layer value can be determined from the pixel in theelemental distribution image. A top layer value can be determined fromthe pixel in the mineral distribution image. A luminosity value can bedetermined from one of the base layer or top layer values. A multiplyvalue can be determined by multiplying the base and top layer values. Anoverlay value can be determined by doubling the multiply value if one ofthe base layer or top layer values is less than a threshold, and byotherwise inverting twice the product of the inverted top layer valuewith the inverted bottom layer value. An image blending weight can bedetermined from the luminosity value. A blended image value can bedetermined as a weighted average of the multiply value and the overlayvalue, wherein the multiply value is weighted by the image blendingweight and the overlay value is weighted by the inverted image blendingweight. The image blending weight can be determined by multiplying theluminosity value by a mixing parameter.

The foregoing has outlined rather broadly the features and technicaladvantages of the present invention in order that the detaileddescription of the invention that follows may be better understood.Additional features and advantages of the invention will be describedhereinafter. It should be appreciated by those skilled in the art thatthe conception and specific embodiments disclosed may be readilyutilized as a basis for modifying or designing other structures forcarrying out the same purposes of the present invention. It should alsobe realized by those skilled in the art that such equivalentconstructions do not depart from the spirit and scope of the inventionas set forth in the appended claims.

Implementations of the invention may also include one or more of thefollowing features. Each pixel in the blended image of the sample can begenerated by determining a top layer value from the pixel in the mineraldistribution image, determining an opacity value from the pixel in theelemental distribution image; and alpha blending the top layer valuewith a black pixel value using the opacity value. The pixel in theblended image can be rendered black when the corresponding pixel in theelemental distribution image has a value that is less than an elementalthreshold.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee. For a more thorough understanding of the presentinvention, and advantages thereof, reference is now made to thefollowing descriptions taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a flow chart depicting a method for blending textural andspatial mineral distribution images.

FIG. 2A is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using themultiply blend mode.

FIG. 2B is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using themultiply blend mode.

FIG. 2C is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using theoverlay blend mode.

FIG. 2D is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using thespecial blend mode.

FIG. 3 is a flow chart depicting a method for blending an elementaldistribution image and a spatial mineral distribution image.

FIG. 4 is an illustration of a blended image made by blending anelemental distribution image with a spatial mineral distribution image.

FIG. 5 is an illustration of a mineral identification and analysissystem.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Proper understanding of the mineralogy of a sample requires knowledge ofboth the texture of the sample and the spatial distribution of mineralsin the sample. Methods are disclosed for forming a blended image bycombining textural and spatial mineral distribution images of a sampleto facilitate an understanding of its mineralogy. In one embodiment, thetwo images can be blended together using known image blendingtechniques, such as by compositing the two images as layers in a blendedimage. The degree of blending can be controlled by assigning a globalopacity value to the alpha channel of the spatial mineral distributionimage. The textural and spatial mineral distribution images can then beblended using a conventional alpha blending algorithm. This approach,however, tends to wash out the colors in the spatial mineraldistribution layer.

Other image blending techniques from the image processing arts can beused to combine the textural and spatial mineral distribution images.For example, the color values of pixels in the textural and spatialmineral distribution images can be added together, subtracted,multiplied or divided by one another to obtain the color values ofcorresponding pixels in the blended image. Since each of these imageblending techniques is globally applied (i.e., identically applied toeach pixel in the blended image), each technique produces a globaleffect on the blended image. For example, multiplying images creates ablended image that is everywhere darker than either of the combinedimages, while dividing images creates a blended image that is everywherelighter than either of the combined images.

Some image blending modes address these global compositing issues bythresholding the image compositing function. That is, different imagecompositing functions are used depending on whether the color value inone of the layers to be blended exceeds a threshold. Examples of suchblending modes include the overlay, hard light, and soft light blending.However, depending on the threshold selected, portions of the image farfrom the threshold may appear too dark or too light.

To optimize the information presented in a blended image consisting oftextural and spatial mineral distribution images from a sample, a customblending method has been developed. This method combines elements fromconventional multiply, screen and overlay blending modes, and mixesthese modes on a per pixel basis as a function of the luminosity of thepixel color in the textural or spatial mineral distribution images.

FIG. 1 is a flow chart depicting a method for blending textural andspatial mineral distribution images. The method begins by receiving atextural image and a spatial mineral distribution image for a sample(100). In one implementation, an optional mixing parameter k that rangesin value between 0 and 1 is also received (110). In someimplementations, the mixing parameter k can be received from a computermemory, where it is set to a default value such as 0.6. In otherimplementations, the mixing parameter can be received as user input,e.g., from a text box or as a radio button selection in a graphical userinterface presented to the user. Setting the mixing parameter k to adefault value of 1 is equivalent to having no mixing parameter at all,as further explained below.

The method proceeds by looping through all of the pixels in the texturaland spatial mineral distribution images (120). If the images havedifferent resolutions, one or both images can be renormalized so theyhave the same resolution or number of pixels. For each pixel, theluminance of the color in either the textural or the spatial mineraldistribution image is determined (130). For example, in oneimplementation, the luminance of the textural image is determined. Ifthis distribution is a grey scale image (e.g., representing theintensity of BSE's), the luminance is simply the grey value for thatpixel. In another implementation, the luminance of the spatial mineraldistribution image is determined. If the pixel color in the spatialmineral distribution image is stored in the RGB or red, green, bluecolor space, the luminance Y can be determined as:

Y=0.299R+0.587G+0.114B  (Eq.1)

If the pixel color is stored in other color spaces, well known formulascan be used to determine the luminance from the pixel's color values inthose color spaces. By normalization, the luminance lies within a rangeof 0 to 1.

Next, pixel values for the conventional multiply, screen and overlayblending modes are obtained from the pixel values of the textural andspatial mineral distribution images. The multiply blend value is simplythe product of the pixel values from these two images:

M _(ij)(l)=T _(ij)(l)*SMD _(ij)(l)  (Eq. 2)

where M_(ij)(l), T_(ij)(l), and SMD_(ij)(l) are respectively themultiply blend value, the textural image value, and the spatial mineraldistribution image value for the lth color component of pixel (i,j). Thepixel value in the multiply blend will be darker than the pixel valuesin either the textural or spatial mineral distribution images. This modeis therefore useful for blending high intensity areas of the texturaland spatial mineral distribution images; however, low intensity areasoften come out too dark to provide useful information about themineralogy of the sample. The screen blend pixel value SCR_(ij)(l) isobtained by inverting the product of the inverted pixel values from thetextural and spatial mineral distribution images. It can be computed as:

SCR _(ij)(l)=1−(1−T _(ij)(l))*(1−SMD _(ij)(l))  (Eq. 3)

The pixel value in the screen blend will be lighter than the pixelvalues in either the textural or spatial mineral distribution images.This mode is therefore useful for low intensity areas of the texturaland spatial mineral distribution images; however, high intensity areasoften come out too light to provide useful information about themineralogy of the sample. Finally, the overlay blend value O_(ij)(l) canbe computed as:

$\begin{matrix}{{O_{ij}(l)} = \{ \begin{matrix}{M_{ij}(l)} & {{{if}\mspace{14mu} {{SMD}_{ij}(l)}} < 0.5} \\{{SCR}_{ij}(l)} & {otherwise}\end{matrix} } & ( {{Eq}.\mspace{14mu} 4} )\end{matrix}$

The overlay blend mode attempts to address the shortcomings in themultiply and screen blend modes when dealing with very bright or darkcompositing layers by selecting the multiply mode if the pixel value isless than a threshold and the screen mode if the pixel value is greaterthan the threshold.

Next, a special blend value is calculated for the pixel in the blendedimage from the multiply and overlay pixel values, the mixing parameterk, and the luminosity of the pixel color in either the textural or thespatial mineral distribution image. The special blend value is computedas a weighted average of the multiply and overlay pixel values, wherethe weights are determined by the product r=k*Y, where k is the optionalmixing parameter and Y is the luminosity of the pixel color in thetextural or spatial mineral distribution image. Thus, the special blendSB_(ij)(l) value of the pixel is given by:

SB_(ij)(l)=rM _(ij)(l)+(1−r)O _(ij)(l)  (Eq. 5)

Since both the mixing parameter k and the pixel intensity Y arenormalized to lie in the range of 0 to 1, the weight r also lies in therange of 0 to 1. Note that when the special mixing parameter k=1, theweight r reduces to the luminosity Y as if there were no mixingparameter k.

As a weighted average, the special blend provides for continuousblending of the textural and spatial mineral distribution images, withno thresholding. It produces a blended image that is optimized for bothlow and high luminosity areas in the spatial mineral distribution image,when blended with both low and high luminosity areas in the texturalimage. High intensity colors in the spatial mineral distribution image(e.g., bright reds and yellows) retain their chrominance in the blendedimage and are easily discernible, even when blended with high intensityareas of the textural image. Moreover, high intensity colors in thespatial mineral distribution image retain their luminance in the blendedimage, even when blended with low intensity areas of the textural image.

FIG. 2A is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using themultiply blend mode. The blended textural and spatial mineraldistribution images were made for a sample containing the mineralspyrite (bright yellow) and rutile (red). As seen in the illustration,while the multiply blend image preservers the colors of the pyrite andrutile minerals, it is everywhere else too dark, and fails to preserveuseful information from the textural and the spatial mineraldistribution images.

FIG. 2B is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using thescreen blend mode. As seen in the illustration, the screen blend imageis lighter than the multiply blend image shown in FIG. 2A. As a result,more information from both the textural and spatial mineral distributionimages is revealed. Nonetheless, the high luminosity colors for pyriteand rutile are significantly faded, resulting in a loss of informationfrom the spatial mineral distribution image.

FIG. 2C is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using theoverlay blend mode. As seen in the illustration, the overlay blend imageis lighter than the multiply blend image shown in FIG. 2A, but darkerthan the screen blend image shown in FIG. 2B. As a result, moreinformation from both the textural and spatial mineral distributionimages is revealed in the overlay blended image. Nonetheless, the highluminosity colors for pyrite and rutile remain faded in the blendedimage, resulting in a loss of information from the spatial mineraldistribution image.

FIG. 2D is an illustration of a blended image made by blending atextural image with a spatial mineral distribution image using thespecial blend mode. As seen in the illustration, the special blend imageis lighter than the multiply blend image shown in FIG. 2A, but darkerthan the overlay blend image shown in FIG. 2C. As a result, moreinformation from both the textural and spatial mineral distributionimages is revealed. Moreover, the high luminosity colors for pyrite andrutile continue to stand out in the special blend image, preservinginformation about these minerals in the sample recorded in the spatialmineral distribution image

The methods disclosed herein can be used to blend any type of texturaland spatial mineral distribution information. In some embodiments, thetextural image can be a per pixel record of the total number of x-raysemitted by the sample at the pixel scan position. In this embodiment,the blended image can provide a user with information regardingconfidence in the mineral identification for the sample. For example, ifa sample's spatial mineral distribution image contained a large monotonered area (indicating the identification of rutile in some area of thesample), and the sample's textural image (composed of per-pixel x-raycounts) showed large counts in the center of the red area but smallercounts toward its edges, the corresponding area in the blended imagewould be different shades of red corresponding to the different x-raypixel counts in the textural image. Where the x-ray count is larger(e.g., in the center), the red in the blended image would be a brightershade than in those areas where the x-ray count is smaller (e.g., alongthe edges). Since the likelihood of correctly identifying a mineral isproportional to the x-ray count, areas of the blended image having abrighter shade of a pure mineral color (e.g., red, blue) will be areasof higher mineral identification confidence, while areas of the blendedimage having a darker shade of a pure mineral color will be areas oflower mineral identification confidence.

FIG. 3 is a flow chart depicting, in yet another embodiment of theinvention, a method for blending elemental and mineral distributionimages. Both the elemental distribution image and the mineraldistribution image can be obtained by analyzing, on a per pixel basis,the energy distribution of x-rays emitted from a sample when bombardedwith charged particles. As shown in FIG. 3, the process begins by rasterscanning the sample (300). Next, for each pixel in the image (310), anx-ray emission spectrum is obtained (320). This sample x-ray emissionspectrum is then analyzed to determine both the elemental (330) and themineral (340) composition of the sample at the pixel scan position. Thisprocess is repeated (350), for all pixels or scan positions for thesample.

As disclosed in U.S. patent application Ser. No. 12/866,697, filed onFeb. 6, 2009, which is herein incorporated by reference, the elementaldistribution image for the sample can be obtained by fitting, on a perpixel basis, the sample's x-ray emission spectrum with elemental x-rayemission spectra to determine the number, kind, and percentage ofelements present in the sample. The elemental composition so determinedcan then be used to determine the mineral composition and the mineraldistribution of the sample. Moreover, for each element present in thesample, an elemental distribution image can be generated by assigning agrayscale value to each pixel in the image corresponding to thepercentage of the element found in the sample at that pixel location orscan position.

As disclosed in U.S. patent application Ser. No. 14/073,523, filed onNov. 6, 2013, which is herein incorporated by reference in its entirety,the spatial mineral distribution image for the sample can also beobtained by fitting, on a per pixel basis, the sample's x-ray emissionspectra with mineral x-ray emission spectra to determine the number,kind, and percentage of minerals present in the sample. Each mineraldetermined to be present in the sample can be assigned a color, and whenmore than one mineral is present the colors can be blended together,with the amount of blend determined by the percentage of mineral presentin the sample.

Referring again to FIG. 3, once the elemental and mineral distributionimages are obtained, an elemental selection can be received. Forexample, the process can present a user with a list of elements andminerals identified in the sample, and with the percentages of each. Theuser can then select a particular element for forming a blended image.For example, the user can select the element by entering its name in atext box, or by selecting a radio button or check box associated withthe element in a graphical user interface. Once selected, the elementaldistribution image and the mineral distribution image can be combined.

In one embodiment, the elemental and mineral distribution images can becombined using the special blend mode discussed above in reference toFIG. 1. In other embodiments, the elemental and mineral distributionimages can be combined using any conventional image blending function.For example, the grayscale value recorded in the elemental distributionimage can be used to determine the opacity of the mineral distributionimage. In this way, the blended image will only contain areas of themineral distribution image that overlap with areas in the elementaldistribution image. All other areas of the blended image will appearblack. Moreover, the shades of colors assigned to minerals identified inthe mineral distribution image will vary in the blended image dependingon the percentage of the selected element found in the elementaldistribution image. Identified mineral colors in the blended image willbe brightest in those areas corresponding to elemental distributionimage areas having a high percentage of the selected element, anddarkest in those areas corresponding to elemental distribution imageareas having a low percentage of the selected element.

In some embodiments, when an element selection is received in step 360,an elemental threshold can be optionally received. In these embodiments,pixels in the elemental distribution image will only be blended withpixels in the mineral distribution image if they are above the elementalthreshold. For example, if the received elemental threshold is 12%, theblended image will only contain pixels in the mineral distribution imageblended with pixels in the elemental distribution image that have agrayscale value greater than 0.12. Pixels in the elemental distributionimage having a grayscale value less than 0.12 will appear black in theblended image. In this way, regions of interest, defined as regionshaving an elemental composition greater than a specified thresholdvalue, can be easily defined and displayed.

FIG. 4 is an illustration of a blended image made by blending anelemental distribution image with a mineral distribution image. In FIG.4, the selected element is magnesium (Mg), and the blended image showsareas of a sample where Mg is present in either dolomite (blue) or clay(green). As seen from the image, different shades of green indicateareas containing clays (identified in the mineral distribution image)having different percentages of Mg (determined from elementaldistribution image).

FIG. 5 is an illustration of a mineral identification and analysissystem 200. The mineral identification system 200 includes a scanningelectron beam system 241, an x-ray detector 240, and a secondary orbackscatter electron detector 243. An electron beam 232 emitted from acathode 253 is accelerated toward an anode 254. Electron beam 232 issubsequently focused to a fine spot by means of a condensing lens 256and an objective lens 258, and can be deflected across a sample 202 bymeans of a deflection coil 260 to perform a two-dimensional raster scanof the sample. The condensing lens 256, objective lens 258, anddeflection coil 260 are supplied current by a power supply 245 operatedunder the control of a system controller 233. The sample 202 ispreferably mounted on a movable X-Y stage 204 within a lower vacuumchamber 210. The vacuum chamber 210 is evacuated to high vacuum by amechanical pumping system 269 and an ion pump 268 operated under thecontrol of vacuum controller 232.

When the electron beam 232 strikes the sample 202, several forms ofradiation are emitted, including backscattered electrons from theelectron beam 232, secondary electrons produced by interactions betweenthe electron beam 232 and the sample, and x-rays produced byinteractions between the electron beam 232 and the sample that arecharacteristic of the elements in the sample. The back scatteredelectrons are detected by an electron detector 242, which outputs asignal indicative of the flux or intensity of back scattered electrons.This signal is received by processor 220. The x-rays emitted from thesample are detected by x-ray detector 240, which preferably outputs asignal indicative of the energy of the detected x-rays. To that end,x-ray detector 240 is preferably an energy dispersive detector such as asilicon drift detector. The output signal of x-ray detector 240 can beamplified and received by processor 220. The back-scattered or secondaryelectrons are detected by an electron detector, such as a

Processor 220 can be programmed to store, for each scanned pixel, acount of the number of back scattered electrons detected, a count of thenumber of x-rays detected, and a histogram counting the number of x-raysdetected in each of a plurality of energy bins over some energy range.Typically, the energy range is on the order of 0-10 keV, and issubdivided into energy bins of 10-20 eV, for a total of 500 to 1000energy bins or channels per pixel.

System 200 also includes a display 244 for displaying the results of thetextural and mineral analysis and other information by way of videocircuit 242; a program memory 222 for storing executable computerprogram code to program the processor 220, and a data memory 223/224 forstoring data, such as per-pixel BSE counts, x-ray counts, and x-rayemission spectra recorded from sample 202, and a library of standardizedelemental or mineral x-ray emission spectra. Program memory 222 caninclude computer storage media in the form of removable and/ornon-removable, volatile and/or nonvolatile memory and can providestorage of computer-readable instructions, data structures, programmodules and other data. Generally, the processor 220 is programmed bymeans of instructions stored in the various computer-readable storagemedia. Programs and operating systems are typically distributed, forexample, on floppy disks or CD-ROMs. From there, they are installed orloaded into the secondary memory of a computer. At execution, they areloaded at least partially into the computer's primary electronic memory.The invention described herein includes these and other various types ofcomputer-readable storage media when such media contain instructions orprograms for implementing the steps described above in conjunction witha microprocessor or other data processor. The invention also includesthe computer itself when programmed according to the methods andtechniques described herein.

While the embodiment shown uses a scanning electron microscope togenerate x-rays from sample 202, other embodiments could employ atransmission electron microscope or a scanning transmission electronmicroscope. An x-ray fluorescence system could also be used to generatex-rays from sample 202. In other embodiments, different forms ofcharacteristic radiation emitted from the sample, such as gamma rays,may be detected.

Although much of the previous description is directed at mineral samplesfrom drill cores, the invention could be used to prepare samples of anysuitable material. The terms “work piece,” “sample,” “substrate,” and“specimen” are used interchangeably in this application unless otherwiseindicated. Further, whenever the terms “automatic,” “automated,” orsimilar terms are used herein, those terms will be understood to includemanual initiation of the automatic or automated process or step.

In the following discussion and in the claims, the terms “including” and“comprising” are used in an open-ended fashion, and thus should beinterpreted to mean “including, but not limited to . . . . ” To theextent that any term is not specially defined in this specification, theintent is that the term be given its plain and ordinary meaning. Theaccompanying drawings are intended to aid in understanding the presentinvention and, unless otherwise indicated, are not drawn to scale.Particle beam systems suitable for carrying out the present inventionare commercially available, for example, from FEI Company, the assigneeof the present application.

Although the present invention and its advantages have been described indetail, it should be understood that various changes, substitutions andalterations can be made to the embodiments described herein withoutdeparting from the spirit and scope of the invention as defined by theappended claims. Moreover, the scope of the present application is notintended to be limited to the particular embodiments of the process,machine, manufacture, composition of matter, means, methods and stepsdescribed in the specification. As one of ordinary skill in the art willreadily appreciate from the disclosure of the present invention,processes, machines, manufacture, compositions of matter, means,methods, or steps, presently existing or later to be developed thatperform substantially the same function or achieve substantially thesame result as the corresponding embodiments described herein may beutilized according to the present invention. Accordingly, the appendedclaims are intended to include within their scope such processes,machines, manufacture, compositions of matter, means, methods, or steps.

We claim as follows:
 1. A method of sample analysis, comprising:directing a charged particle beam toward a work piece; detectingemissions from the work piece at multiple points impacted by the chargedparticle beam; using the emissions to determine the base layermineralogy image and the top layer mineralogy image. receiving a baselayer mineralogy image and a top layer mineralogy image; sequentially,via a computer processor and for each pixel in the base or top layermineralogy images: determining a base layer value from the pixel in thebase layer mineralogy image; determining a top layer value from thepixel in the top layer mineralogy image; determining a luminosity valuefrom one of the base layer or top layer values; and determining ablended image value for each pixel of a blended image using one ofmultiple blending methods, the blending method used being determined ona per pixel basis as a function of the luminosity; and generating ablended mineralogy image using the blended image values; and displayingthe blended image.
 2. The sample analysis method of claim 1 in whichdirecting a charged particle beam toward a work piece comprisesdirecting an electron beam toward the work piece and in which detectingemissions from the work piece at multiple points impacted by the chargedparticle beam comprises detecting backscattered electrons and x-rays. 3.The computer implemented method of claim 1, in which the multipleblending methods include at least two blending methods selected from thegroup of multiple blend modes, screen blend modes, and overlay blendmodes.
 4. The computer implemented method of claim 1, in which themultiple blending methods include at least two blending methods selectedfrom the group of overlay blend modes, hard light blending, and softlight blending.
 5. A computer implemented method for blending mineralogyimages, comprising: receiving a base layer mineralogy image and a toplayer mineralogy image; sequentially, via a computer processor and foreach pixel in the base or top layer mineralogy images: determining abase layer value from the pixel in the base layer mineralogy image;determining a top layer value from the pixel in the top layer mineralogyimage; determining a luminosity value from one of the base layer or toplayer values; and determining a blended image value for each pixel of ablended image using one of multiple blending methods, the blendingmethod used being determined on a per pixel basis as a function of theluminosity; and generating a blended mineralogy image using the blendedimage values; and displaying the blended image.
 6. The computerimplemented method of claim 5, in which the multiple blending methodsinclude at least two blending methods selected from the group ofmultiple blend modes, screen blend modes, and overlay blend modes. 7.The computer implemented method of claim 5, in which the multipleblending methods include at least two blending methods selected from thegroup of overlay blend modes, hard light blending, and soft lightblending.
 8. The computer implemented method of claim 5, wherein one ofthe base layer mineralogy image or the top layer mineralogy image is atextural mineralogy image and the other of the base layer or the toplayer is a spatial mineral distribution image.
 9. The computerimplemented method of claim 5, wherein the textural mineralogy image isan image of the intensity of back-scattered electrons.
 10. The computerimplemented method of claim 9, wherein the luminance value for a pixelin the textural mineralogy image comprises the grey value for thatpixel.
 11. The computer implemented method of claim 5, wherein one ofthe base layer mineralogy image or the top layer mineralogy image is anelemental image and the other of the base layer or the top layer is aspatial mineral distribution image.
 12. The computer implemented methodof claim 11, in which determining a blended image includes receiving anelemental threshold and assigning a blended image value to render apixel black when a corresponding pixel of the elemental distributionimage has a value that is less than the elemental threshold.
 13. Thecomputer implemented method of claim 5, wherein the base layermineralogy image is an image of the intensity of generated x-rays andthe top layer mineralogy image is a spatial mineral distribution image.14. The computer implemented method of claim 5, wherein the blendedvalue is determined by doubling the multiply value if the top layervalue is less than a threshold, and by otherwise inverting twice theproduct of the inverted top layer value with the inverted bottom layervalue.
 15. The computer implemented method of claim 5, furthercomprising: directing a charged particle beam toward a work piece;detecting emissions from the work piece at multiple points impacted bythe charged particle beam; using the emissions to determine the baselayer mineralogy image and the top layer mineralogy image.
 16. Thecomputer implemented method of claim 5, in which determining a blendedimage value includes receiving a mixing parameter; and determining animage blending weight by multiplying the luminosity value by the mixingparameter.
 17. A computer program product, stored on a non-transitorycomputer readable medium, comprising instructions operable to cause aprogrammable processor to perform the method of claim
 5. 18. Thecomputer program product of claim 17, in which the computer instructionsfor determining a blended image value for each pixel of a blended imageusing one of multiple blending methods include computer instructions inwhich the multiple blend modes include at least two blend modes selectedfrom the group of a multiple blend mode, a screen blend mode, and anoverlay blend mode.
 19. The computer program product of claim 17, inwhich the computer instructions for determining a blended image valuefor each pixel of a blended image using one of multiple blending methodsinclude computer instructions in which the multiple blend modes includeat least two blend modes selected from the group of overlay blend mode,hard light blending, and soft light blending.
 20. An charged particlebeam apparatus, comprising: an electron beam source for illuminating aportion of the sample; at least one detector for detecting radiationemitted from the illuminated portion of the sample; and one or moreprocessors configured to: determine from the detected radiation a baselayer mineralogy image and a top layer mineralogy image; sequentially,via a computer processor and for each pixel in the base or top layermineralogy images: determine a base layer value from the pixel in thebase layer mineralogy image; determine a top layer value from the pixelin the top layer mineralogy image; determine a luminosity value from oneof the base layer or top layer values; determine a blended image valueusing one of multiple blending methods, the blending method used beingdetermined on a per pixel basis as a function of the luminosity; andgenerate a blended mineralogy image using the blended image values; anddisplay the blended image.
 21. The charged particle beam apparatus ofclaim 20, in which the at least one detector comprises a backscatteredelectron detector and an x-ray detector and in which one of the base ortop layer mineralogy images comprises a backscattered electron image.